Dr.-Ing. Yazhou Zhu

Research Interests

Wireless radar sensing/positioning signal processing, including Wi-Fi Human Sensing, FMCW Radar Sensing
Integrated Sensing and Communications (ISAC)
Wireless/satellite (NTN) communication (algorithm/protocol)
Wi-Fi IEEE 802.11bq (Integrated mmWave) Pre-standardization
IEEE 802.11 Next-Generation Wi‑Fi Standard, Wi-Fi IEEE 802.11bn Standard Amendment
Signal optimization algorithm
Applied AI/ML
Software development with Python/TensorFlow/C/C++/matlab
5G/6G and Non-Terrestrial Networks (NTN)
3GPP standards for 5G Massive MIMO, NTN
Resource optimization
Massive MIMO-OFDM transceiver prototyping

The Most Important 802.11 Contributions

802.11bn (Official Documents)

11-26/0724r1 – LB291 comment resolutions (PPDU encoding, 38.3.16.3.6/3.8), currently waiting for SP
11-26/0043r3 – LB291 comment resolutions (TX requirements, 38.3.19)

Additional Internal Intel Contributions

IMMW beamforming training refinements (11bq), GS-based constellation shaping (internal Intel Wi-Fi 9), AMPDU PHY/MAC impairment analysis (internal Intel Wi-Fi 9), and PHY simulation support for 11bq and Wi-Fi 9. These were part of Intel's internal contribution pipeline and therefore not publicly listed under my name.

Selected Publications

[1] Y. Zhu, R. Shah, M. Pinaroc, and V. Frascolla, “AI-Driven Human Wi-Fi Sensing with Hierarchical Architecture and Breathing Detection,” in Proc. WiSense 2026: 3rd Int. Workshop on Pervasive Wireless Sensing and Edge Computing. (Accepted)

[2] Y. Zhu, R. Shah, and V. Frascolla, “OFDM-Based mmWave Wi-Fi ISAC System Implementation for Monostatic Human Sensing,” in Proc. 2026 20th European Conference on Antennas and Propagation (EuCAP), Regular Session Papers, 2026. (Accepted)

[3] Y. Zhu, R. C. Shah, U. Parker, and V. Frascolla, “Interference Leakage Impact on Monostatic mmWave Sensing: OFDM Vs FMCW Performance with Practical Passive Isolation,” in EuCNC & 20th European Conference on Antennas and Propagation (EuCAP), 2026. (Accepted)

[4] Y. Zhu, C. A. Hofmann, and A. Knopp, “Machine Learning-based Flexible Payload Power Resource Allocation for Non-orthogonal SATCOM,” 2022 IEEE Global Communications Conference (GLOBECOM), 2022, pp. 1–6.

[5] Y. Zhu, C. A. Hofmann, and A. Knopp, “Ka-Band LEO Satellite Internet of Things Channel Characterization: Survey and Measurement,” ICC 2022 – IEEE International Conference on Communications, 2022, pp. 1–6.

[6] Y. Zhu, C. A. Hofmann, and A. Knopp, “Distributed Resource Optimization for NOMA Transmission in Beamforming SATCOM,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 4, pp. 1190–1209, Apr. 2022. (Best Publication 2022 Award)

[7] Y. Zhu, C. A. Hofmann and A. Knopp, “Performance Optimization for Multi-Gateway NOMA-Beamforming in Multi-Beam SATCOM,” ICC 2021 – IEEE International Conference on Communications, 2021, pp. 1–6.

[8] Y. Zhu, T. Delamotte and A. Knopp, “Geographical NOMA-Beamforming in Multi-Beam Satellite-Based Internet of Things,” 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1–6.

[9] Y. Zhu, Q. Zhang and T. Yang, “Low-Complexity Hybrid Precoding With Dynamic Beam Assignment in mmWave OFDM Systems,” IEEE Transactions on Vehicular Technology, vol. 67, no. 4, pp. 3685–3689, Apr. 2018.

[10] Y. Zhu and T. Yang, “Low Complexity Hybrid Beamforming for Uplink Multiuser mmWave MIMO Systems,” 2017 IEEE Wireless Communications and Networking Conference (WCNC), 2017, pp. 1–6.

[11] Y. Zhu and T. Yang, “Near-optimal practical convergent method for interference alignment in MIMO interference channels,” Electronics Letters, vol. 52, no. 25, pp. 2074–2076, 2016.

[12] Y. Zhu, F. Zheng, and T. Kaiser, “Distributed transmission framework against quasi-static channel impairments for downlink multi-cell MIMO-OFDM with IA,” 18th Int. OFDM-Workshop 2014 (InOWo14), Essen, Germany, 27–28 Aug. 2014.

Real-time AI Human Sensing via Wi-Fi Respiration on Commercial PC with Intel COST IEEE 802.11ax Chip

This demonstration showcases a real-time AI human sensing system using Wi-Fi respiration signals captured on a commercial PC equipped with an Intel COST-compliant IEEE 802.11ax (Wi-Fi 6) chip. The system performs live breathing detection and human presence sensing by processing Channel State Information (CSI) data, leveraging a hierarchical deep-learning architecture for robust and low-latency inference directly on commodity hardware.

Recording: Real-time AI Human Sensing demo — July 16, 2025

About Me